Prediction and Optimization of Emission Characteristics and Fuel Economy for Methanol-Diesel RCCI Engine
Based on the test data of methanol-diesel dual-fuel reactivity controlled compression ignition(RCCI)engine bench,the prediction intelligent model of emission and fuel economy was established based on BP neural network optimized by the par-ticle swarm optimization(PSO)algorithm.The inputs of model were engine load,methanol replacement rate and EGR rate,and the outputs of model were NOx,smoke,CO,THC emission and equivalent effective fuel consumption.The determination coefficients of model were 0.99,0.97,0.99,0.98 and 0.96 respectively,and the mean absolute percentage errors were 6.46％,0.56％,3.12％,1.21％and 0.3％,which indicated that the constructed PSO-BPNN model could effectively predict the NOx,smoke,CO,THC emissions and economy of methanol-diesel RCCI engine.Based on the partial least square regression(PLSR),the correlation of different control parameters to engine pollutant emissions and economy was analyzed.The PSO-BPNN prediction model was combined with the NSGA-Ⅱ algorithm,the collaborative optimization of load,methanol replace-ment rate and EGR rate was conducted based on the objective of NOx,smoke and equivalent effective fuel consumption,and the optimal control parameter combination was finally calibrated to the control system of methanol-diesel dual-fuel engine for the experimental verification.The results showed that the optimized smoke had little change,NOx emissions reduced by 39.6％,and the equivalent effective fuel consumption reduced by 2.6％.
万方数据
维普
